{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第二周作业"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 首先 import 必要的模块\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn import metrics  \n",
    "import matplotlib.pyplot as pyplot\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1)读取数据 & 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"diabetes.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "# 字段说明\n",
    "# Pregnancies: 怀孕次数\n",
    "# Glucose: 口服葡萄糖耐受试验中,2 小时的血浆葡萄糖浓度。\n",
    "# BloodPressure: 舒张压(mm Hg)\n",
    "# SkinThickness: 三头肌皮肤褶层厚度(mm)\n",
    "# Insulin:2 小时血清胰岛素含量(μU/ ml)\n",
    "# BMI: 体重指数(体重,kg /(身高,m)^ 2)\n",
    "# DiabetesPedigreeFunction: 糖尿病家族史\n",
    "# Age: 年龄(岁)\n",
    "# Outcome: 输出变了/类别标签(0 或 1,出现糖尿病为 1, 否则为 0)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 各属性的统计特性\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 查看是否有空值\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols=data.columns \n",
    "\n",
    "data_corr = data.corr().abs()\n",
    "pyplot.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outcome 分布，看看各类样本分布是否均衡\n",
    "sns.countplot(data.Outcome);\n",
    "pyplot.xlabel('Outcome');\n",
    "pyplot.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [小结]：\n",
    "## ①数据共768个样本，每个样本包含8个特征，其中BMI和DiabetesPedigreeFunction为float64类型，其他为int64类型，y为Outcome，为0的样本数量大约是为1样本数量的2倍;\n",
    "## ②数据中无缺失值，无需进行缺失值填充"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2)特征编码&数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从原始数据中分离输入特征x和输出y\n",
    "X = data.drop('Outcome', axis = 1)\n",
    "y = data['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "X = ss_X.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state = 0)\n",
    "#print('X_train.shape: %s %s'%X_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3)Logistic 回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for classifier GridSearchCV(cv=5, error_score='raise',\n",
      "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False),\n",
      "       fit_params=None, iid=True, n_jobs=1,\n",
      "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.01, 0.1, 1]},\n",
      "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
      "       scoring='accuracy', verbose=0):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.84      0.92      0.88       107\n",
      "          1       0.76      0.62      0.68        47\n",
      "\n",
      "avg / total       0.82      0.82      0.82       154\n",
      "\n",
      "\n",
      "Confusion matrix:\n",
      "[[98  9]\n",
      " [18 29]]\n",
      "grid.best_score \t:\t0.760586319218241\n",
      "grid.best_params_\t:\t{'C': 1, 'penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "\n",
    "lr_penaltys = LogisticRegression()\n",
    "#需要调优的参数 penalty 和 C \n",
    "penaltys = ['l1','l2']\n",
    "Cs = [ 0.01, 0.1, 1]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "#grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='neg_log_loss')\n",
    "grid = GridSearchCV(lr_penaltys, tuned_parameters,cv=5, scoring='accuracy') \n",
    "# examine the best model\n",
    "grid.fit(X_train,y_train) \n",
    "\n",
    "y_predict = grid.predict(X_test)\n",
    "\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\"\n",
    "      % (grid, classification_report(y_test,y_predict)))\n",
    "print(\"Confusion matrix:\\n%s\" % confusion_matrix(y_test, y_predict))\n",
    "print('grid.best_score \\t:\\t%s' % grid.best_score_)\n",
    "print('grid.best_params_\\t:\\t%s' % grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4)线性 SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "# 定义函数，对一个C使用线性SVM进行训练，并校验打印出评分\n",
    "def fit_grid_point_Linear(C, X_train, y_train, X_val, y_val):\n",
    "    \n",
    "    # 在训练集是那个利用SVC训练\n",
    "    SVC2 =  LinearSVC( C = C)\n",
    "    SVC2 = SVC2.fit(X_train, y_train)\n",
    "    \n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC2.score(X_val, y_val)\n",
    "    print(\"[LinearSVC]accuracy: {}\".format(accuracy)+\"\\t,C:{},\".format(C))\n",
    "    return accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LinearSVC]accuracy: 0.7857142857142857\t,C:0.001,\n",
      "[LinearSVC]accuracy: 0.8246753246753247\t,C:0.01,\n",
      "[LinearSVC]accuracy: 0.8246753246753247\t,C:0.1,\n",
      "[LinearSVC]accuracy: 0.8246753246753247\t,C:1.0,\n",
      "[LinearSVC]accuracy: 0.8246753246753247\t,C:10.0,\n",
      "[LinearSVC]accuracy: 0.7272727272727273\t,C:100.0,\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "C_s = np.logspace(-3, 2, 6)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份  \n",
    "#penalty_s = ['l1','l2'] \n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "#    for j, penalty in enumerate(penalty_s):\n",
    "    tmp = fit_grid_point_Linear(oneC, X_train, y_train, X_test, y_test)\n",
    "    accuracy_s.append(tmp)\n",
    "\n",
    "x_axis = np.log10(C_s)\n",
    "#for j, penalty in enumerate(penalty_s):\n",
    "pyplot.plot(x_axis, np.array(accuracy_s), 'b-')\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'accuracy' )\n",
    "#pyplot.savefig('SVM_Otto.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "logistic回归可调整参数C和正则，当C=1，penalty=l1时模型得到较好的准确率（0.7605）;线性SVM可以调整参数C，当C=1时模型得到较好的准确率（0.8246）;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5)RBF 核的 SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "# 定义函数，对一对C和gamma，使用RBF核SVM进行训练，并校验打印出评分\n",
    "def fit_grid_point_RBF(C, gamma, X_train, y_train, X_val, y_val):\n",
    "    \n",
    "    # 在训练集是那个利用SVC训练\n",
    "    SVC3 = SVC( C = C, kernel='rbf', gamma = gamma)\n",
    "    SVC3 = SVC3.fit(X_train, y_train)\n",
    "    \n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC3.score(X_val, y_val)\n",
    "    \n",
    "    print(\"[RBFSVC]accuracy: {}\".format(accuracy)+\"\\tgamma:{}\\t\".format(gamma)+\"C:{},\".format(C))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:0.001\tC:0.001,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:0.01\tC:0.001,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:0.1\tC:0.001,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:1.0\tC:0.001,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:10.0\tC:0.001,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:100.0\tC:0.001,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:0.001\tC:0.01,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:0.01\tC:0.01,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:0.1\tC:0.01,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:1.0\tC:0.01,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:10.0\tC:0.01,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:100.0\tC:0.01,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:0.001\tC:0.1,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:0.01\tC:0.1,\n",
      "[RBFSVC]accuracy: 0.8051948051948052\tgamma:0.1\tC:0.1,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:1.0\tC:0.1,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:10.0\tC:0.1,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:100.0\tC:0.1,\n",
      "[RBFSVC]accuracy: 0.7012987012987013\tgamma:0.001\tC:1.0,\n",
      "[RBFSVC]accuracy: 0.8246753246753247\tgamma:0.01\tC:1.0,\n",
      "[RBFSVC]accuracy: 0.7792207792207793\tgamma:0.1\tC:1.0,\n",
      "[RBFSVC]accuracy: 0.7597402597402597\tgamma:1.0\tC:1.0,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:10.0\tC:1.0,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:100.0\tC:1.0,\n",
      "[RBFSVC]accuracy: 0.8181818181818182\tgamma:0.001\tC:10.0,\n",
      "[RBFSVC]accuracy: 0.8181818181818182\tgamma:0.01\tC:10.0,\n",
      "[RBFSVC]accuracy: 0.7662337662337663\tgamma:0.1\tC:10.0,\n",
      "[RBFSVC]accuracy: 0.7077922077922078\tgamma:1.0\tC:10.0,\n",
      "[RBFSVC]accuracy: 0.6883116883116883\tgamma:10.0\tC:10.0,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:100.0\tC:10.0,\n",
      "[RBFSVC]accuracy: 0.8246753246753247\tgamma:0.001\tC:100.0,\n",
      "[RBFSVC]accuracy: 0.7922077922077922\tgamma:0.01\tC:100.0,\n",
      "[RBFSVC]accuracy: 0.7532467532467533\tgamma:0.1\tC:100.0,\n",
      "[RBFSVC]accuracy: 0.7077922077922078\tgamma:1.0\tC:100.0,\n",
      "[RBFSVC]accuracy: 0.6883116883116883\tgamma:10.0\tC:100.0,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:100.0\tC:100.0,\n"
     ]
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "C_s = np.logspace(-3,2,6)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份 \n",
    "gamma_s = np.logspace(-3, 2, 6)  \n",
    "\n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    for j, gamma in enumerate(gamma_s):\n",
    "        tmp = fit_grid_point_RBF(oneC, gamma, X_train, y_train, X_test, y_test)\n",
    "        accuracy_s.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_s1 =np.array(accuracy_s).reshape(len(C_s),len(gamma_s))\n",
    "x_axis = np.log10(C_s)\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    pyplot.plot(x_axis, np.array(accuracy_s1[:,j]), label = ' Test - log(gamma)' + str(np.log10(gamma)))\n",
    "\n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'accuracy' )\n",
    "#pyplot.savefig('RBF_SVM_Otto.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "gamma取2时无变化，取-2时较好，适当调小gamma，以获取更好的性能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:0.0001\tC:0.001,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:0.0031622776601683794\tC:0.001,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:0.1\tC:0.001,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:0.0001\tC:0.01,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:0.0031622776601683794\tC:0.01,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:0.1\tC:0.01,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:0.0001\tC:0.1,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:0.0031622776601683794\tC:0.1,\n",
      "[RBFSVC]accuracy: 0.8051948051948052\tgamma:0.1\tC:0.1,\n",
      "[RBFSVC]accuracy: 0.6948051948051948\tgamma:0.0001\tC:1.0,\n",
      "[RBFSVC]accuracy: 0.8246753246753247\tgamma:0.0031622776601683794\tC:1.0,\n",
      "[RBFSVC]accuracy: 0.7792207792207793\tgamma:0.1\tC:1.0,\n",
      "[RBFSVC]accuracy: 0.7012987012987013\tgamma:0.0001\tC:10.0,\n",
      "[RBFSVC]accuracy: 0.8181818181818182\tgamma:0.0031622776601683794\tC:10.0,\n",
      "[RBFSVC]accuracy: 0.7662337662337663\tgamma:0.1\tC:10.0,\n",
      "[RBFSVC]accuracy: 0.8181818181818182\tgamma:0.0001\tC:100.0,\n",
      "[RBFSVC]accuracy: 0.8116883116883117\tgamma:0.0031622776601683794\tC:100.0,\n",
      "[RBFSVC]accuracy: 0.7532467532467533\tgamma:0.1\tC:100.0,\n"
     ]
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "C_s = np.logspace(-3,2,6)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份 \n",
    "gamma_s = np.logspace(-4, -1, 3)  \n",
    "\n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    for j, gamma in enumerate(gamma_s):\n",
    "        tmp = fit_grid_point_RBF(oneC, gamma, X_train, y_train, X_test, y_test)\n",
    "        accuracy_s.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_s1 =np.array(accuracy_s).reshape(len(C_s),len(gamma_s))\n",
    "x_axis = np.log10(C_s)\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    pyplot.plot(x_axis, np.array(accuracy_s1[:,j]), label = ' Test - log(gamma)' + str(np.log10(gamma)))\n",
    "\n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'accuracy' )\n",
    "#pyplot.savefig('RBF_SVM_Otto.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  }
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